The project goal is to contribute to knowledge about explanatory patterns in clinical medicine. The aim of the proposed research is to document and analyze the content of explanations generated by physicians when they justify their diagnostic and treatment decisions. It is hypothesized that medical explanations are a component of the medical decision making process, and are affected both by differences in physician characteristics (e.g., expertise, confidence), and by dimensions of the problem structure (e.g., referring physician's diagnosis, presence of disconfirming diagnostic cues). Fixed-order experimentally controlled medical problems will be used to collect physician-generated clinical explanations. Differences in these explanations will be analyzed using content analyses and repeat measures analysis of variance procedures. The proposed research combines the knowledge and methodology of human information processing research with the work on artificial intelligence and expert medical computing systems. Both kinds of research are being actively pursued at Stanford, and this project has the support of faculty members from both groups. The Study results have implications for improving the explanation capabilities of computer-based medical consultation, and may be useful for designing medical education programs directed at improving traditional consultations given by specialists to other physicians.